Re: High Energy Price Sensitivity Run – Reference Case
Bibliographic record
Abstract
The following describes the ENERGY 2020 model outputs for a sensitivity run in which energy supply prices were increased by 50 % from levels used in the Reference Case. These cost increases were applied to the world price of oil, the well head price for natural gas, as well as coal and biomass prices. For this scenario, the model was set up to assume that new generation would be built to meet reserve margin requirements. Two policy cases were modeled under this high price scenario: 1) the Reference Case, which includes the impacts of the Energy Independence and Security Act (EISA), and 2) Policy Case 01 which includes all policies approved for modeling by the Task Force except for the Cap & Trade policy. The results presented below compare the Reference Case with 50 % higher energy prices to the original Reference Case. The original Reference Case used for comparison was consistent with the case described in the memo dated April 16, 2008 but using model results prior to feedback from the REMI model. More detailed results have been provided to the TAG in the form of Excel spreadsheets which summarize changes resulting for Wisconsin, the surrounding states and the rest of the US and Canada. The results of modeling Policy Case 01 with high energy prices are presented in a separate memorandum. The data inputs and assumptions underlying this Reference Case are described in the Assumptions Book. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".